Influent BOD Soft-sensing And Operation Energy Consumption Analysis Model Of Wastewater Treatment Plant

Wastewater treatment plants are the important urban infrastructures as well as the energy-intensive industries,and the huge energy consumption and low energy utilization have become problems that cannot be ignored in the sewage treatment industry.The increasingly stringent environmental protection requirements and the ever-increasing sewage discharge have put greater pressure on the energy consumption of the sewage plant,and put forward higher requirements for the operation and management of the sewage plant.The influent conditions of the sewage treatment plant are related to the operating parameters.Real-time measurement of influent conditions,reasonable analysis and systematic understanding of the energy consumption level of sewage treatment plants to optimize control and management strategies have far-reaching practical significance for improving sewage treatment efficiency,reducing operating energy consumption,and promoting the sustainable development of the sewage treatment industry.In view of the inability to detect the critical water quality index BOD₅ in time and the randomness and ambiguity in the analysis of energy consumption in the sewage plant.In this paper,the influent BOD₅ soft sensing models are established based on LM-BP,Grid Search-SVR,PSO-SVR and GA-SVR.At the same time,the cloud model method is proposed to analyze the energy consumption of the sewage plant,and the energy consumption of the sewage plant under different influent quality and quantity conditions is analyzed and data mining is performed to optimize the control management strategy.According to the established models,this paper takes a sewage treatment plant in Heilongjiang Province as an example,combined with the operation data of sewage treatment plant,and carries out model verification and feasibility analysis.First,the research on the soft sensing model of influent BOD₅ is conducted.Based on correlation analysis,influent COD,SS,NH₄⁺-N,TN,and TP are selected as auxiliary variables for the influent BOD₅soft-sensing modeling.According to the correlation analysis between influent BOD₅ and influent COD,SS,NH₄⁺-N,TN,TP,it is found that these five influent water quality indicators have a certain correlation with influent BOD₅,which can be selected as auxiliary variables for BOD₅ soft sensor modeling.According to the analysis of the results of LM-BP based on the connection weight method,it is found that for the prediction of influent BOD₅,the most important influent indicators are NH₄⁺-N,COD and TN,and the indicators with the least contribution are TP and SS.In the case of using the same training set and test set for modeling,it is found that compared with the BP network,the SVR model has better stability and generalization,and the overall performance of the SVR model optimized based on GA is the best.The correlation coefficients of the training set,test set and overall data set are 0.84,0.81,and0.83,respectively,indicating that the influent BOD₅ soft-sensing model of GA-SVR has certain practicability and can be used as the soft redundancy of hardware instruments,and has certain guiding function for optimizing the operation and management of sewage treatment plant.Then,a cloud model analysis of the energy consumption of the sewage treatment plant based on the classification of the influent conditions is carried out.Based on the data of the influent load,principal component analysis and K-means clustering method were applied to classify the influent conditions of sewage plants.Based on energy consumption data,according to the bilateral constraint and the golden ratio method,the energy consumption of the sewage treatment plant is divided into 5 standard evaluation levels,the physical interpretation of the cloud digital characteristics in the energy consumption evaluation process of the wastewater treatment plant is clarified,and the energy consumption standard cloud model is established.A multi-step backward cloud transformation algorithm is used to values of corresponding energy consumption under different influent conditions of cloud numerical characteristics,and the forward cloud transformation algorithm is used to simulate the energy consumption distribution under each influent circumstances.The analysis shows that the energy consumption of the sewage treatment plant is the highest when the influent concentration is low and the inflow volume is small,and it crosses four energy consumption levels,indicating that the operation and management of the sewage plant is abnormal in this case.Finally,a better energy consumption distribution is found through the clustering method,and the corresponding control parameters and operating strategies can be used as an empirical reference for the regulation and control of the wastewater treatment plant to guide the optimized operation of the wastewater treatment plant.